1. Field of the Invention
The present invention relates to a method and apparatus for detecting and demapping coded signals in data communication and broadcasting systems, and more particularly to a method and apparatus for receiving and demodulating received signals with the aid of channel state information (CSI) in coded orthogonal frequency-division multiplexing (COFDM) based wireless communication and broadcasting systems.
2. Description of Related Art
COFDM has become a popular technique for transmission of signals over wired and wireless channels. COFDM has been adopted in several transmission standards such as digital audio broadcasting (DAB), digital video broadcasting (DVB), the IEEE 802.11a wireless local area network (WLAN) standard (see reference [1]: “WLAN MAC and PHY Specification: High-speed Physical Layer in the 5 GHz Band, IEEE Std 802.11a Supplement to IEEE Std Part 11, September 1999.”) and the IEEE 802.16 wireless metropolitan area network (WMAN) standard (see reference [2]: “IEEE Standard for Local and Metropolitan Area Networks—Part 16: Air Interface for Fixed Broadband Wireless Access Systems, IEEE Std. 802.16, 2004.”). Recently, multi-band COFDM based ultra wideband (UWB) systems have been proposed for achieving wireless transmission with very high data rate, as described in reference [3]: “WiMedia MBOA, MultiBand OFDM Physical Layer Specification, Ver. 1.1.5, Jul. 14, 2006”. COFDM is also being pursued for dedicated short-range communications (DSRC) for road side to vehicle communications and as a potential candidate for fourth-generation (4G) mobile wireless systems.
In wireless communication and broadcasting systems, high-speed transmission of signals with wide bandwidth normally suffers from severe frequency selective fading. This can be avoided in an OFDM system by transforming the signal into a number of orthogonal components, each of these OFDM components having a bandwidth less than the coherence bandwidth of the transmission channel. By modulating these OFDM signal components onto different subcarriers, the transmission in each individual subcarrier experiences only frequency flat fading. The forward error correction (FEC) coding to transmitted information streams is thus employed to further combat the fading on OFDM subcarriers.
A typical COFDM baseband system is depicted in FIG. 1. In the transmitter, binary input data are encoded by an FEC channel encoder 1. Depending on the requirement of each individual application, the channel encoding can be convolutional coding, Turbo coding, low density parity check (LDPC) coding, or any other applicable FEC coding. The coding rate can be adjusted by puncturing the coded output bits to accommodate the desired data rate. After bit interleaving in a bit interleaver 2, the encoded binary data, {s(n)}, are mapped onto the modulation constellations in a modulation mapper 3. The modulation mode can be binary phase-shift keying (BPSK), quadrature phase-shift keying (QPSK), quadrature amplitude modulation (QAM), or whichever applicable. The constellation-mapped complex values, {xk} k=0, 1, . . . , N−1, are then sent to an N-point inverse discrete Fourier transform (IDFT) processor 5 for further OFDM modulation. In some COFDM systems, such as in the recently published China national standard for digital television terrestrial broadcasting system (see reference [4]: “GB 20600-2006, China Standard, Framing structure, channel coding and modulation for digital television terrestrial broadcasting system, August 2006.”), the constellation-mapped complex values may first be sent to a symbol interleaver 4 before the IDFT processing. Although whether or not to use a symbol interleaver in a COFDM system is case by case, a symbol interleaver 4 has been purposely included in the embodiments of this invention for demonstrating its generality, as shown in FIG. 1.
Referring to FIG. 1, in the receiver, after performing the appropriate timing and frequency synchronization, a discrete Fourier transform (DFT) processor 7 receives the OFDM symbol from a demultiplexer 6 and performs DFT so as to convert each time-domain OFDM symbol into N frequency-domain complex values, {yk} k=0, 1, . . . , N−1. Ideally, these values should be same as {xk}, but they are usually distorted by the transmission channel and receiver noise. Thus, before being used as the input to a symbol de-interleaver 9 and a modulation demapper 10, the distorted values, {yk}, should be compensated first by an equalizer 8 so that the equalized values, {zk}, are good estimates of {xk}. The compensation is usually performed in frequency-domain by using the estimation of channel frequency response (CFR). After this coherent detection, {zk} orderly pass through the symbol de-interleaver 9 and the modulation demapper 10 for undertaking symbol de-interleaving and constellation demapping. Finally, the resultant values, {r(n)}, are bit-wise de-interleaved and channel decoded by a bit-wise de-interleaver 11 and a channel decoder 12, respectively, for recovery of the transmitted information bits. It should be noted that, here, the term “bit-wise de-interleaver” instead of “bit de-interleaver” has been used due to the fact that a soft-decision demapper is usually involved for enhancing the error correction capability of the subsequent channel decoder and thus the input to this de-interleaver, {r(n)}, may not be necessarily binary values.
As shown in FIG. 1, the transmission channel is modeled as a multipath fading channel with channel impulse response (CIR), h(t), and is corrupted by an additive noise, v(t). Using a discrete-time equivalent baseband model, h(t) and v(t) can be expressed as their frequency domain counterparts, h and v, respectively. Here, h=[h0, h1, . . . , hN−1]T is the CFR vector and v=[v0, v1, . . . , vN−1]T is a vector of independent identically distributed complex zero-mean Gaussian noise with variance σv2. Let x=[x0, x1, . . . , xN−1]T be the transmitted signal vector (i.e., the input of IDFT), and y=[y0, y1, . . . , yN−1]T be the received vector (i.e., the output of DFT), the OFDM signal model can be simply expressed byy=Xh+v  (1)where X is a diagonal matrix whose diagonal contains the transmitted signal vector, x. In a COFDM receiver system, where coherent detection is necessary for providing the subsequent channel decoder with the properly demodulated constellation signals, the channel estimation and tracking are important. There exist several methods for estimating the CFR in an OFDM system. The simplest one is the well known least-square (LS) estimate which is given by
                                          h            ^                    ls                =                                            X                              -                1                                      ⁢            y                    =                                                    [                                                                                                                              y                          0                                                                          x                          0                                                                                                                                                              y                          1                                                                          x                          1                                                                                                            …                                                                                                                y                                                      N                            -                            1                                                                                                    x                                                      N                            -                            1                                                                                                                                              ]                            T                        .                                              (        2        )            Equalization based on the LS estimator of (2) is the result of an optimization based on the zero-forcing (ZF) criterion which aims at canceling intercarrier interference (ICI) regardless of the noise level. To minimize the combined effect of ICI and additive noise, a more sophisticated solution called linear minimum mean-squared error (LMMSE) estimator can be used, as described in reference [5]: “J. J. van de Beek, O. Edfors, M. Sandell, S. K. Wilson, and P. O. Börjesson, “On channel estimation in OFDM systems,” in Proc. IEEE Vehicular Technology Conf., vol. 2, Chicago, Ill., July 1995, pp. 815-819”. The LMMSE estimate of CFR h in (1), given the received data y and the transmitted symbols X, isĥlmmse=Rhh[Rhh+σv2(XXH)−1]−1ĥls  (3)where the superscript (•)H denotes Hermitian transpose and Rhh=E{hhH} is the channel autocorrelation matrix. The LMMSE estimator of (3) is of considerable complexity since inverting a matrix is required once the data in X are updated. The complexity can be reduced to some extent by adopting a simplified LMMSE estimator, as described in reference [6]: “O. Edfors, M. Sandell, J. J. van de Beek, S. K. Wilson, and P. O. Börjesson, “OFDM channel estimation by singular value decomposition,” IEEE Trans. Commun., vol. 46, July 1998, pp. 931-939”. By assuming the same signal constellations on all subcarriers and equal probability on all constellation points and defining the average signal to noise ratio (SNR) as E{|xk|2}/σv2, the simplified LMMSE estimator is given by
                                          h            ^                    lmmse                =                                                            R                hh                            ⁡                              (                                                      R                    hh                                    +                                                            β                      SNR                                        ⁢                    I                                                  )                                                    -              1                                ⁢                                    h              ^                        ls                                              (        4        )            where I is the identity matrix and β=E{|xk|2}E{|1/xk|2} is a constant depending on the signal constellation. Obviously, in addition to the high complexity, both LMMSE estimators of (3) and (4) require the knowledge of channel statistics Rhh and SNR. This may prevent use of LMMSE in practical implementation when the required information either is unknown or can not be easily estimated. In reference [7]: “L. Deneire, P. Vandenameele, L. V d. Perre, B. Gyselinckx, and M. Engels, “A low complexity ML channel estimator for OFDM,” IEEE Trans. Commun., vol. 51, February 2003, pp. 135-140.”, a maximum-likelihood (ML) estimator, which is of lower complexity and less dependence of the knowledge of Rhh and SNR, was proposed. With the assumption of the channel order of L, the ML estimator takes the following formĥml=Fh(FhHFh)−1FhHĥls  (5)where Fh represents the first L columns of a N×N DFT matrix, F. By this setting, the detrimental effect of additive noise to the CFR estimate can be substantially reduced but not completely removed. As a result, equalization using the ML channel estimator of (5) is usually superior to that using the LS estimator of (2), but is inferior to that using the LMMSE estimator of (3) or (4) in terms of signal recovery capability.
With the obtained channel estimation vector, ĥ=[ĥ0, ĥ1, . . . , ĥN−1]T, which can be one of ĥls, ĥlmmse and ĥml, the frequency-domain equalization in a COFDM system takes the form of a complex divider bank at the DFT output in the receiver, i.e.,zk=yk/ĥk, k=0, 1, . . . , N−1.  (6)As a result, the equalizer used in a COFDM system is usually called one-tap equalizer.
A well-known issue involved in the equalization based on the LS estimator of (2) is that the ZF criterion does not have a solution if the channel transfer function has spectral nulls in the signal bandwidth. Inversion of the CFR requires an infinite gain and leads to infinite noise enhancement at those frequencies corresponding to spectral nulls, as described in reference [8]: “H. Sari, G. Karam, and I. Jeanclaude, “Transmission techniques for digital terrestrial TV broadcasting”, IEEE Commun. Mag., vol. 33, no. 2, February 1995, pp. 100-109.”. Similar situation occurs when some subcarriers have experienced deep fading. To some extent, the equalization based on the ML estimator of (5) also suffers from the deep-fading caused problem since, as mentioned before, it has not taken full consideration of the effect of additive noise.
Inclusion of powerful FEC coding and interleaving in the OFDM system is the primary solution to overcome the problem of deep notches occurring in the received OFDM signal spectrum. With the availability of more and more powerful channel coding and decoding techniques (e.g., LDPC coding), this solution has proved to be very effective in practice. In addition, to make full use of the decoding capability of the channel decoder, the channel state information (CSI) aided decoding strategy has been suggested in some literature. Examples can be found in references: [8]-[14], [15]-[16], [17]-[19] when convolutional coding, Turbo coding and LDPC coding are used for FEC, respectively, wherein [9]: “M.-Y. Park and W.-C. Lee, “A demapping method using the pilots in COFDM systems,” IEEE Trans. Consumer Electronics, vol. 44, no. 3, August 1998, pp. 1150-1153.”; [10]: “W.-C. Lee, H.-M. Park, K.-J. Kang and K.-B. Kim, “Performance analysis of Viterbi decoder using channel state information in COFDM system,” IEEE Trans. Broadcasting, vol. 44, no. 4, December 1998, pp. 488-496.”; [11]: “W.-C. Lee, H.-M. Park and J.-S. Park, “Viterbi decoding method using channel state information in COFDM system,” IEEE Trans. Consumer Electronics, vol. 45, no. 3, August 1999, pp. 533-537.”; [12]: “S. Armour, A. Nix and D. Bull, “Use of linear transverse equalisers and channel state information in combined OFDM-equalization,” in Proc. IEEE Int. Symp. on Personal, Indoor and Mobile Radio Communications (PIMRC), vol. 1, London, UK, September 2000, pp. 615-619.”; [13]: “M. R. G. Butler, S. Armour, P. N. Fletcher-, A. R. Nix, and D. R. Bull, “Viterbi decoding strategies for 5 GHz wireless LAN systems,” in Proc. IEEE 54th Veh. Technol. Conf, VTC 2001—Fall, Atlantic City, USA, October 2001, pp. 77-81.”; [14]: “Y. Wang, J. Ge, B. Ai, P. Liu and S. Y Yang, “A soft decision decoding scheme for wireless COFDM with application to DVB-T,” IEEE Trans. Consumer Electronics, vol. 50, no. 1, February 2004, pp. 84-88.”; [15]: “H. Shin, S. Kim, and J. H. Lee, “Turbo decoding in a Rayleigh fading channel with estimated channel state information,” in. Proc. IEEE 52nd Veh. Technol. Conf., VTC 2000—Fall, Boston, Mass., USA, September 2000, pp. 1358-1363.”; [16]: “M. L. Ammari and F. Gagnon, “Iterative channel estimation and decoding of Turbo-coded OFDM symbols in selective Rayleigh channel,” Canadian Journal of Elect. Comput. Eng., vol. 32, no. 1, Winter 2007, pp. 9-18.”; [17]: “H. Niu, M. Shen, J. A. Ritcey and H. Liu, “Iterative channel estimation and LDPC decoding over flat-fading channels,” in Proc. Conf. on Info. Sciences and Systems, The Johns Hopkins University, Mar. 12-14, 2003.”; [18]: “M.-K. Oh; Y-H. Kwon, J.-H. Park and D.-J. Park, “Blind iterative channel estimation and LDPC decoding for OFDM systems,” in Proc. IEEE 61st Veh. Technol. Conf, VTC 2005-Spring, Stockholm, Sweden, 30 May-1 Jun. 2005, pp. 514-517.”; and [19]: “H. Niu, M. Shen, J. A. Ritcey and H. Liu, “A factor graph approach to iterative channel estimation and LDPC decoding over fading channels,” IEEE Trans. Wireless Commun., vol. 4, no. 4, July 2005, pp. 1345-1350.”.
FIG. 2 shows an apparatus 100 for receiving coded signals with the aid of CSI. The transmitted signal yk from a DFT processor 101, is applied to a CSI estimator 102 and a one-tap equalizer 103. In the one-tap equalizer 103, the transmitted signal yk is compensated by the estimate of CFR, ĥk, obtained from the CSI estimator 102. On the other hand, the CSI estimator 102 obtains the CSI estimation which is used in aid to the channel decoder 106 for achieving performance enhanced decoding. Denote by ĉk the CSI estimation on subcarrier k. Depending on each individual algorithm used and/or system performance requirement, the CSI estimation on subcarrier k, ĉk, can be the squared magnitude of CFR, SNR, noise variance, channel estimation error variance on subcarrier k, or even their combination. It should be noted that, in FIG. 2, the one-tap equalization using CFR, ĥk, and the channel decoding with the aid of CSI estimation, ĉk, are performed separately.
The way to apply the CSI estimation to the decoding process depends on each type of channel decoder. When a convolutional encoder is adopted in the transmitter, the receiver performs maximum-likelihood sequence decoding using the well-known Viterbi algorithm, which searches for the most likely path (the path with the smallest metric, or Euclidian distance, from the received noisy and distorted signal) in the code trellis. In this case, each subcarrier related metric can be weighted by its corresponding CSI estimation. In the decoding of Turbo codes or LDPC codes, an iterative process is usually required, and, in each iteration, the channel reliability information (log-likelihood ratio) is updated and used for the next iteration. In this case, the CSI estimation can be used to weight the channel reliability information.
In fact, although the involved decoding processes may be different, the above mentioned CSI-aided decoding schemes can be treated as being equivalent to using the CSI estimation to linearly weight the input signal of the decoder, i.e., the output signal of the constellation demapper. This has been explicitly shown in [14] (for Viterbi decoder), [15] (for Turbo decoder) and [18] (for LDPC decoder). Being aware of this, one may find that the weighting operations can actually be shifted to the one-tap equalizer. As shown in reference [20]: “W. Li, Z. Wang, Y Yan, M. Tomisawa, “An efficient low-cost LS equalization in COFDM based UWB systems by utilizing channel-state-information (CSI),” in Proc. IEEE 62nd Veh. Technol. Conf., VTC 2005-Fall, Dallas, Tex., USA, September 2005, pp. 2167-2171.”, by this way, the complexity involved in equalization can be reduced if the squared magnitude of the estimated CFR, |ĥr|2, is used as the CSI estimation on subcarrier k. Also, the additional symbol de-interleaver 104 and the bit-wise de-interleaver 105 required for reordering the CSI estimations in FIG. 2 become unnecessary. In this case, the structure of the apparatus 100 in FIG. 2 can be simplified as shown in FIG. 3. Mathematically, the CSI-aided one-tap equalizer 203 of the apparatus 200 shown in FIG. 3 can be expressed as:
                              z          k                =                                                            y                k                                                              h                  ^                                k                                      ·                                                                                                h                    ^                                    k                                                            2                                =                                                    y                k                            ⁡                              [                                                      h                    ^                                    k                                ]                                      *                                              (        7        )            for k=0, 1, . . . , N−1,where [•]* denotes complex conjugate.
Weighting the equalized signal, yk/ĥk, on each subcarrier by the corresponding squared channel attenuation factor, |ĥk|2, in (7) can be interpreted as the dual of equalizing the channel in the sense that equalization consists of amplifying an attenuated received signal to match it to the nominal decision levels, whereas weighting consists of matching the decision levels to the received signal attenuation. Weighting in this way clearly avoids the noise enhancement inherent to equalized OFDM systems since a small weighting factor is associated to the equalized signals with low reliability, and a large weighting factor is associated to the equalized signals with high reliability.
Mathematically, the CSI-aided, one-tap equalizer of (7) can be obtained by multiplying the received signal, yk, by the conjugate of the channel estimation, [ĥk]*, and thus a low-complexity divider-free implementation can be expected. However, when applying (7) to the actual implementation of a COFDM system, this divider-free solution for computational saving may become problematic. This can be explained as follows. As mentioned before, in order to enhance the error correction capability of the channel decoder, a soft-decision demapper is usually involved. The soft-decision demapping implies that the output of demapper (input of channel decoder) should be quantized. The level of quantization accuracy will be limited to a small number of bits. Under this circumstance, the weighting factors (squared channel attenuation factor), |ĥk|2, need to be normalized, as described in [13]. When the modulation mapping is BPSK or QPSK, the normalization can be either performed in the equalization stage or embedded in the quantization process of the demapper. Both methods require division operations. As a result, the computational saving in the apparatus 200 of FIG. 3 may not be that significant when compared with that in the apparatus 100 of FIG. 2.
Another problem appears when applying the simplified CSI-aided one-tap equalizer of (7) in a COFDM system where the modulation mode is amplitude dependent such as QAM. Basically, the simplified CSI-aided one-tap equalizer 203 of (7) can not work in this case. This is true even when the above mentioned normalization process is added. All these drawbacks have limited the usage of the apparatus 200 of FIG. 3 in practice.